{"id":21802443,"url":"https://github.com/qengineering/jetson-nano-image","last_synced_at":"2025-04-05T12:05:20.412Z","repository":{"id":48627749,"uuid":"380203666","full_name":"Qengineering/Jetson-Nano-image","owner":"Qengineering","description":"Jetson Nano image with deep learning frameworks","archived":false,"fork":false,"pushed_at":"2024-11-20T11:01:23.000Z","size":66,"stargazers_count":135,"open_issues_count":19,"forks_count":24,"subscribers_count":9,"default_branch":"main","last_synced_at":"2025-03-29T11:07:29.679Z","etag":null,"topics":["cuda","deep-learning","jetson-nano","mnn","ncnn","opencv","pytorch","sd-card-image","team-viewer","tegra","tensorflow","torch","torchvision"],"latest_commit_sha":null,"homepage":"https://qengineering.eu/install-tensorflow-2.4.0-on-jetson-nano.html","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Qengineering.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-06-25T10:29:17.000Z","updated_at":"2025-03-14T04:47:32.000Z","dependencies_parsed_at":"2024-07-24T12:55:21.799Z","dependency_job_id":"b0249d41-7ee8-4e25-ac0e-1b91b5f3cf85","html_url":"https://github.com/Qengineering/Jetson-Nano-image","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FJetson-Nano-image","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FJetson-Nano-image/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FJetson-Nano-image/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FJetson-Nano-image/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Qengineering","download_url":"https://codeload.github.com/Qengineering/Jetson-Nano-image/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247332602,"owners_count":20921853,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cuda","deep-learning","jetson-nano","mnn","ncnn","opencv","pytorch","sd-card-image","team-viewer","tegra","tensorflow","torch","torchvision"],"created_at":"2024-11-27T11:28:40.497Z","updated_at":"2025-04-05T12:05:20.392Z","avatar_url":"https://github.com/Qengineering.png","language":null,"funding_links":["https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick\u0026hosted_button_id=CPZTM5BB3FCYL"],"categories":[],"sub_categories":[],"readme":"# Jetson Nano DNN image\n![output image]( https://qengineering.eu/images/SDcard32GBJetson.webp )\u003cbr/\u003e\n## A Jetson Nano image with OpenCV, TensorFlow and Pytorch\n[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)\u003cbr/\u003e\u003cbr/\u003e\n\n------------\n\n## Installation.\n\n- Get a 32 GB (minimal) SD-card which will hold the image. \n- Download the `JetsonNano.img.xz` image (**7.2 GByte!**) from our [Sync](https://ln5.sync.com/dl/4bdb25370/xewd7xqz-6khtpcs2-w7zais3c-8uqi2rcj) site. \n- Flash the image on the SD card with the [Imager](https://www.raspberrypi.org/software/) or [balenaEtcher](https://www.balena.io/etcher/).\n- According to [issue #17](https://github.com/Qengineering/Jetson-Nano-image/issues/17#) only flash the xz directly, not an unzipped img image.\n- Insert the SD card in your Jetson Nano **4 GB RAM** and enjoy.\n- Username: ***jetson***\n- Password: ***jetson***\n- JetsonNano.img.xz md5sum: 621F2E4E0B7775E5293E2186C96E91AA\n\n### GDrive.\nIn some parts of the world, getting a good solid connection to Sync is difficult. That is why we've also provided a copy on [Google Drive](https://drive.google.com/file/d/1xSKJQX2uuLI-ewShU8LP8FROC6ozyiwo/view?usp=sharing). However, Google Drive limits the number of daily downloads. Please be considerate and use Google Drive only if necessary.\n\n------------\n\n## Tips.\n\n* If you are in need of extra space, you can delete the opencv and the opencv_contrib folder from the SD card. They are no longer needed since all libraries are placed in the /usr/ directory.\n* Use a tool like [GParted](https://gparted.org/) `sudo apt-get install gparted` to expand the image to larger SD cards. We recommend a minimum of 64 GB. Deep learning simply requires a lot of space.\u003cbr/\u003e\u003cbr/\u003e\n\n------------\n\n## Pre-installed frameworks.\n\n- [JetPack](https://developer.nvidia.com/embedded/jetpack) 4.6.0\n- [OpenCV](https://qengineering.eu/deep-learning-with-opencv-on-raspberry-pi-4.html) 4.5.3\n- [TensorFLow](https://qengineering.eu/install-tensorflow-2.4.0-on-raspberry-64-os.html) 2.4.1\n- [TensorFlow Addons](https://qengineering.eu/install-tensorflow-2.4.0-on-raspberry-64-os.html) 0.13.0-dev\n- [Pytorch](https://qengineering.eu/install-pytorch-on-raspberry-pi-4.html) 1.8.1\n- [TorchVision](https://qengineering.eu/install-pytorch-on-raspberry-pi-4.html) 0.9.1\n- [LibTorch](https://qengineering.eu/install-pytorch-on-raspberry-pi-4.html) 1.8.1 \n- [ncnn](https://qengineering.eu/install-ncnn-on-jetson-nano.html) 20210720\n- [MNN](https://qengineering.eu/install-mnn-on-jetson-nano.html) 1.2.1\n- [JTOP](https://github.com/rbonghi/jetson_stats) 3.1.1 \n- [TeamViewer aarch64](https://www.teamviewer.com/en/download/linux/) 15.24.5\n\nTensorflow 2.5 and above require CUDA 11. CUDA version 11 cannot be installed on a Jetson Nano due to incompatibility between the GPU and low-level software at this time, hence Tensorflow 2.4.1. Only when NVIDIA releases a JetPack with CUDA 11 will we be able to upgrade Tensorflow.\n\n![output image]( https://qengineering.eu/images/Software_Jetson.png )\u003cbr/\u003e\u003cbr/\u003e\n![output image]( https://qengineering.eu/images/JTOP_jetson.png )\n\n------------\n\n## OpenCV + TensorFlow.\n\nImporting both TensorFlow and OpenCV in Python can throw the error: _cannot allocate memory in static TLS block_.\u003cbr/\u003e\nThis behaviour only occurs on an aarch64 system and is caused by the OpenMP memory requirements not being met.\u003cbr/\u003e\nFor more information, see GitHub ticket [#14884](https://github.com/opencv/opencv/issues/14884).\u003cbr/\u003e\n\n![output image](https://qengineering.eu/images/SwapImportOpenCVJetson.webp)\n\nThere are a few solutions. The easiest is to import OpenCV at the beginning, as shown above.\u003cbr/\u003e\nThe other is disabling OpenMP by setting the -DBUILD_OPENMP and -DWITH_OPENMP flags OFF.\u003cbr/\u003e\nWhere possible, OpenCV will now use the default pthread or the TBB engine for parallelization.\u003cbr/\u003e\nWe don't recommend it. Not all OpenCV algorithms automatically switch to pthread.\u003cbr/\u003e\nOur advice is to import OpenCV into Python first before anything else.\u003cbr/\u003e\n\n------------\n\n[![paypal](https://qengineering.eu/images/TipJarSmall4.png)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick\u0026hosted_button_id=CPZTM5BB3FCYL) \n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Fjetson-nano-image","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqengineering%2Fjetson-nano-image","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Fjetson-nano-image/lists"}